Predicting of Areas with Ecotourism Capability Using Artificial Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Talebi Manijeh; MAJNOUNIAN BARIS; MAKHDOUM MAJID; ABDI EHSAN; OMID MAHMOUD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: ENVIRONMENTAL RESEARCHES Year:2021 | Volume:12 | Issue:23 Page(s): 55-66

Download Full-Text

Persian Verion

View:

119

Download:

Cites:

Information Journal Paper

Title

Predicting of Areas with Ecotourism Capability Using Artificial Neural Network

Author(s)

Keywords

Ecological capability evaluation

Ecotourism

Systemic analysis

Artificial Neural Network

Arasbaran Protected Area

Abstract

Recreational use of the area must be performed conforms to its ecological capability. Therefore, this study was carried out with the aim of providing a method for modelling and ranking the areas with Ecotourism capability. For this purpose, Makhdoum systemic method, regarding to the region specifications, and multi-layer perceptron Artificial Neural Network (MLP) were used to evaluate the ecological capability of Arasbaran Protected Area. At first, ecological and socio-economic resources were identified and maps of them were provided. Then, Ecotourism capability map was prepared by analyzing and overlaying of data in ArcGIS. In the next step, using the results of the systemic method, neural network was trained and its various structures were evaluated. Finally, map of the suitable tourism areas was modeled based on neural network output. In the end, using the socio-economic criteria and recreational attractions, prioritize and final evaluation was performed. Regarding to the Systemic analysis, the area has the capability for intensive recreation class-2 (0. 06%), and extensive recreation class-2 (10. 33%). Topology 7-9-3 was selected as the best classifier with an overall accuracy of 98% for recreational regions classification. The best and the lowest of neural network application were shown to belong to intensive recreation class, and extensive recreation class, respectively. Based on modeled map, 0. 17%, 10. 09%, and 89. 74% of the area were shown to belong to intensive recreation-class 2, extensive recreation-class 2, and unsuitable for recreation, respectively. This study showed Artificial Neural Network has potential for classification of the suitable tourism areas with high accuracy.

Cites

No record.

References

No record.

Cite

APA: Copy

Talebi, Manijeh, MAJNOUNIAN, BARIS, MAKHDOUM, MAJID, ABDI, EHSAN, & OMID, MAHMOUD. (2021). Predicting of Areas with Ecotourism Capability Using Artificial Neural Network. ENVIRONMENTAL RESEARCHES, 12(23 ), 55-66. SID. https://sid.ir/paper/986042/en

Vancouver: Copy

Talebi Manijeh, MAJNOUNIAN BARIS, MAKHDOUM MAJID, ABDI EHSAN, OMID MAHMOUD. Predicting of Areas with Ecotourism Capability Using Artificial Neural Network. ENVIRONMENTAL RESEARCHES[Internet]. 2021;12(23 ):55-66. Available from: https://sid.ir/paper/986042/en

IEEE: Copy

Manijeh Talebi, BARIS MAJNOUNIAN, MAJID MAKHDOUM, EHSAN ABDI, and MAHMOUD OMID, “Predicting of Areas with Ecotourism Capability Using Artificial Neural Network,” ENVIRONMENTAL RESEARCHES, vol. 12, no. 23 , pp. 55–66, 2021, [Online]. Available: https://sid.ir/paper/986042/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops